code: def next_hyperparameter_lowest_confidence(
      fun_prediction,
      fun_prediction_args,
      x_bounds,
      x_types,
      minimize_starting_points,
      minimize_constraints_fun=None,
  ):
      """
      "Lowest Confidence" acquisition function
      """
      best_x = None
      best_acquisition_value = None
      x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
      x_bounds_minmax = numpy.array(x_bounds_minmax)
  
      for starting_point in numpy.array(minimize_starting_points):
          res = minimize(
              fun=_lowest_confidence,
              x0=starting_point.reshape(1, -1),
              bounds=x_bounds_minmax,
              method="L-BFGS-B",
              args=(
                  fun_prediction,
                  fun_prediction_args,
                  x_bounds,
                  x_types,
                  minimize_constraints_fun,
              ),
          )
  
          if (best_acquisition_value) is None or (
              res.fun < best_acquisition_value
          ):
              res.x = numpy.ndarray.tolist(res.x)
              res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
              if (minimize_constraints_fun is None) or (
                  minimize_constraints_fun(res.x) is True
              ):
                  best_acquisition_value = res.fun
                  best_x = res.x
  
      outputs = None
      if best_x is not None:
          mu, sigma = fun_prediction(best_x, *fun_prediction_args)
          outputs = {
              "hyperparameter": best_x,
              "expected_mu": mu,
              "expected_sigma": sigma,
              "acquisition_func": "lc",
          }
      return outputs
  
file_path: nni/nni/algorithms/hpo/metis_tuner/lib_acquisition_function.py
imports: import sys
  import numpy
  from scipy.stats import norm
  from scipy.optimize import minimize
  from .None import lib_data
label_desc: Use image node_id 3 for calling a global function with example usage: next_hyperparameter_lowest_confidence(fun_prediction, fun_prediction_args, x_bounds, x_types, minimize_starting_points, minimize_constraints_fun) and returns: outputs